this paper attempts to compare the forecasting performance of the arima model and hybrid arma-garch models by using daily data of the iran’s exchange rate against the u.s. dollar (irr/usd) for the period of 20 march 2014 to 20 june 2015. the period of 20 march 2014 to 19 april 2015 was used to build the model while remaining data were used to do out of sample forecasting and check the forecasti...

This paper attempts to compare the forecasting performance of the ARIMA model and hybrid ARMA-GARCH Models by using daily data of the Iran’s exchange rate against the U.S. Dollar (IRR/USD) for the period of 20 March 2014 to 20 June 2015. The period of 20 March 2014 to 19 April 2015 was used to build the model while remaining data were used to do out of sample forecasting and check the forecasti...

Nonparametric detrending or noise reduction methods are often employed to separate trends from noisy time series when no satisfactory models exist to fit the data. However, conventional noise reduction methods depend on subjective choices of smoothing parameters. Here we present a simple multivariate noise reduction method based on available nonlinear forecasting techniques. These are in turn b...

A novel field of data mining has been spatio-temporal clustering focused on the new methods and techniques, which are able to adapt previous methods and solutions to the new problems. A set of geo-referenced time series are data generated by several devices like GPS, sensor station, cell phones and many other sensing device. This paper defines the the new K-means clustering grouping spatially a...

Long-term demand forecasting presents the first step in planning and developing future generation, transmission and distribution facilities. One of the primary tasks of an electric utility accurately predicts load demand requirements at all times, especially for long-term. Based on the outcome of such forecasts, utilities coordinate their resources to meet the forecasted demand using a least-co...

in this paper we investigate the long memory of tehran securities price index and fit arfima model using 970 daily data since 1382/1/6 until 1386/4/17. furthermore, we compare the forecasting performance of arfima and arima models. the results show that the series is a long memory one and therefore it can become stationary by fractional differencing. we obtaine the fractional differencing param...

modeling and analysis of future prices has been hot topic for economic analysts in recent years. traditionally, the complex movements in the prices are usually taken as random or stochastic process. however, they may be produced by a deterministic nonlinear process. accuracy and efficiency of economic models in the short period forecasting is strategic and crucial for business world. nonlinear ...

Good ATM network cash management requires accurate information of future cash demand. In this paper we compare computational intelligence models when performing cash flow forecasting for one day. Adaptive input selection and model parameter identification are used with every forecasting model in order to perform more flexible comparison. Experimental data contains 200 ATMs from real ATM network...

A class of seasonal space-time models for general lattice systems is proposed. Co-variance properties of spatial rst-order models are studied. Estimation approaches in time series analysis are adopted and forecasting techniques using the seasonal space-time models are discussed. The models are applied to 516 consecutive elds of monthly-averaged 500 mb geopotential heights over a 1010 lattice in...